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Remote role with broad, senior technical requirements at a mid-tier employer yields moderate competition.
Highly specialized data-platform skills (Iceberg, dbt, Airflow, Kubernetes) limit cross-industry transferability.
Explicit 8+ years plus many mandatory platform, cloud, and orchestration skills makes filters strict.
Lead design and implementation of a decoupled Lakehouse data platform architecture on AWS and Kubernetes using Apache Iceberg.
Optimize multi-engine compute workload matching to minimize cloud spend and maximize performance, including DuckDB, PyIceberg, dbt, and serverless query engines.
Engineer automated data governance, cataloging, and quality frameworks with embedded zero-trust security and multi-layer data quality gates.
8+ years in platform architecture or infrastructure engineering with experience building production-grade open-standard data platforms from scratch.
Expertise in Apache Iceberg or similar open table formats with ACID transactions on object storage (S3).
Proficiency in Kubernetes (EKS/K8s) including deploying containerized execution runners, and advanced Apache Airflow for orchestration.
Advanced AWS cloud infrastructure knowledge including S3, EKS, IAM, EC2, and serverless query serving with strict storage-compute separation and FinOps optimization.
Strong architect with deep technical expertise in open-standard data platforms and multi-engine compute environments.
Experience managing complex data governance, metadata catalog integration (Apache Polaris, Glue), and automated data quality enforcement.
Skilled in communicating technical architectural decisions and trade-offs to executive leadership and cross-functional teams.